Methods for background and noise suppression in binary to grayscale image conversion

ABSTRACT

One embodiment is a method for suppressing background inaccuracies in binary to grayscale image conversion. A binary image is converted to a grayscale image using a neighbor map. An image enhancement function is applied to the grayscale image to supress background inaccuracies in the grayscale image. Another embodiment is method for converting a binary pixel of a binary image to a grayscale pixel of a grayscale image and suppressing noise in the grayscale image using selective filtering of the binary image. Another embodiment is a method for converting a binary image to a first grayscale image and suppressing noise in the first grayscale image to produce a noise suppressed grayscale image using selective filtering of the grayscale image.

BACKGROUND

1. Technical Field

The presently disclosed embodiments are directed to methods and systemsfor background suppression and noise suppression in binary to grayscaleimage conversion. More specifically, background inaccuracies produced inbinary to grayscale image conversion are suppressed using an imageenhancement function or tone reproduction curve, and noise created intransition areas during binary to grayscale image conversion issuppressed using a filter.

2. Description of Related Art

Conventionally, a typical black and white image on photographic film,for example, includes various gray levels of light. That is, differentamounts of light are reflected from various spots of the image on thefilm, providing what is known as a continuous tone photographic image.It is conventionally known how to digitize the grayscale continuous tonephotographic image. More specifically, each pixel or spot of thephotographic image is assigned a number representing the amount of lightor gray level of that particular spot. Typically, an eight-bit word isused, giving 256 different digitized gray levels of light. The digitizedimage is known as a continuous tone, or continuous tone, digital image.Further, it is possible to go back and forth between the analog anddigital images and maintain a reasonable reproduction of the image.

It is also conventionally known to provide an image on a recordingmedium, for example, a paper sheet, rather than on photographic film.For example, a modulated laser can be used to scan a xerographic drum togive a series of black and white spots. The spots are formed by turningthe laser on and off. The image on the drum is then developed andtransferred to a copy sheet. This process of developing black and whitespots provides a binary image, but does not generate a continuous toneimage.

It is possible, however, to create the impression of a continuous toneimage by using halftoning. The halftone process uses a mathematicallystored screen pattern or array, for example, which is analmost-sinusoidal two-dimensional pattern. The process converts theoriginal or continuous tone image into an image of black and white spotsthat “appears” to be a continuous tone image. This process is generallyaccomplished by systematically comparing each pixel's continuous tonevalue with the value of the screen. If the continuous tone value of thepixel is less dense than the screen value, then a white spot isproduced. On the other hand, if the pixel value is more dense than thescreen value, a black spot is produced. It should be understood that thepixel values are the 8-bit grayscale values for each pixel of theoriginal image.

In effect, this procedure converts a grayscale image into black andwhite spots, but gives the impression of multiple gray levels byproducing more white spots for a less-dense area and more black spotsfor a denser area. Although a true continuous tone image is not producedby this procedure, the procedure has two advantages. One advantage isthat each spot of the image is described with one bit, rather than theeight-bit word used for each gray level pixel in the original continuoustone picture. This allows the halftone image to be stored withapproximately ⅛ of the storage of the original continuous tone image.Another advantage is that, in fact, a halftone image can be printed onpaper. In other words, the conversion takes each eight-bit pixel valuerepresenting a grayscale value, compares the pixel value to a screenvalue and provides either a zero (0) or a one (1) to modulate the laser.This image can then be printed on a recording medium such as paper.

The use of a tone reproduction curve (TRC) is described in U.S. Pat. No.5,450,502 (hereinafter “the ‘502 patent’”), the disclosure of which isincorporated herein by reference. A TRC is defined as a function thatdescribes the relationship of the input to the output within a systemfor the purposes of image enhancement. This function is then applied tothe full input image.

Currently known techniques to convert binary images to continuous toneimages are described in U.S. Pat. No. 6,343,159 (hereinafter “the ‘159patent’”), the disclosure of which is incorporated herein by reference.These techniques include using a look up table (LUT) to convert one bitvalues in a neighbor of image pixels into grayscale values and using aspatial filter to approximate the original grayscale values.

SUMMARY

One embodiment is a method for converting a binary pixel of a binaryimage to a grayscale pixel of a grayscale image and suppressingbackground inaccuracies in the grayscale image. A binary image isinputted or scanned. A binary pixel of the binary image is associatedwith a neighbor map. The binary pixel is converted to a grayscale pixelbased on the neighbor map. An image enhancement function is applied tothe grayscale pixel to suppress background inaccuracies in the grayscaleimage.

Another embodiment is a method for converting a binary pixel of a binaryimage to a grayscale pixel of a grayscale image and suppressing noise inthe grayscale image using selective filtering of the binary image. Abinary image is inputted or scanned. A binary pixel of the binary imageis associated with a neighbor map. The neighbor map is converted to aweighting value. The neighbor map is filtered to produce a firstgrayscale pixel value. The neighbor map is compared to one or morebinary patterns of a conversion look up table to produce a secondgrayscale pixel value. An inverted weighting value is multiplied by thefirst grayscale pixel value to produce a first weighted grayscale pixelvalue. The weighting value is multiplied by the second grayscale pixelvalue to produce a second weighted grayscale pixel value. The firstweighted grayscale pixel value and the second weighted grayscale pixelvalue are summed to produce the grayscale pixel and suppress noise.

Yet another embodiment is a method for converting a binary image to afirst grayscale image and suppressing noise in the first grayscale imageto produce a noise suppressed grayscale image using selective filteringof the first grayscale image. A binary image is inputted or scanned. Abinary pixel of the binary image is associated with a neighbor map. Eachbinary pixel of the binary image is converted to a first grayscale pixelof a first grayscale image based on the binary neighbor map. A grayscaleneighbor map is created for each first grayscale pixel. Each grayscaleneighbor map is converted to a selection value. Each grayscale neighbormap is filtered to produce a second grayscale pixel. A noise suppressedgrayscale image is produced by selecting each noise suppressed grayscalepixel as the first grayscale pixel if the selection value is a firstselection value and selecting each noise suppressed grayscale pixel asthe second grayscale pixel if the each selection value is a secondselection value.

Other objects, features, and advantages of the present application willbecome apparent from the following detailed description, theaccompanying drawings, and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart showing a method for converting a binary pixel ofa binary image to a grayscale pixel of a grayscale image and suppressingbackground inaccuracies in the grayscale image, in accordance with anembodiment.

FIG. 2 is an exemplary 3×3 neighbor map, in accordance with anembodiment.

FIG. 3 is schematic diagram showing a system for converting a binarypixel of a binary image to a grayscale pixel of a grayscale image andsuppressing noise in the grayscale image using selective filtering ofthe binary image, in accordance with an embodiment.

FIG. 4 is a flowchart showing a method for converting a binary pixel ofa binary image to a grayscale pixel of a grayscale image and suppressingnoise in the grayscale image using selective filtering of the binaryimage, in accordance with an embodiment.

FIG. 5 is schematic diagram showing a system for converting a binaryimage to a first grayscale image and suppressing noise in the firstgrayscale image to produce a noise suppressed grayscale image usingselective filtering of the first grayscale image, in accordance with anembodiment.

FIG. 6 is schematic diagram showing a method for converting a binaryimage to a first grayscale image and suppressing noise in the firstgrayscale image to produce a noise suppressed grayscale image usingselective filtering of the first grayscale image, in accordance with anembodiment.

Before one or more embodiments are described in detail, one skilled inthe art will appreciate that an embodiment is not limited in itsapplication to the details of construction, the arrangements ofcomponents, and the arrangement of steps set forth in the followingdetailed description or illustrated in the drawings. An embodiment iscapable of being practiced or being carried out in various ways. Also,it is to be understood that the phraseology and terminology used hereinis for the purpose of description and should not be regarded aslimiting.

DETAILED DESCRIPTION

Restoration of gray values in an image from one bit values can beaccomplished in many ways. In one of the ways, a filter is applied tothe image to determine the gray value from the local low frequencycontent of a neighborhood of image pixels. A second method is to use alook up table (LUT) to convert the one bit values in a neighborhood ofimage pixels into a gray value as described in the '159 patent.

The LUT approach restores gray well, but often the tone scale is notperfectly reproduced. This is especially evident in the light backgroundareas of an image or in black areas like the inside of large black text.For example, if there are any one bit values in a white background, thederived LUT will yield an eight bit value that is not perfectly white.This causes an image with a white background to have a visiblebackground that is not perfectly white. A white background that is notperfectly white contains background inaccuracies.

In one embodiment, a TRC is used in conjunction with a LUT in a one bitto eight bit conversion process in order to adjust tone characteristicsand suppress background inaccuracies. The TRC is used to adjust the tonescale to compensate for background inaccuracies in the LUT conversion.This tone scale adjustment can include, for example, making very lightvalues completely white.

The TRC is used to eliminate background that is produced by the LUTconversion in the white areas of an image. The TRC adjusts the lightnessin the lighter areas of the image. As described in the '502 patent theTRC is an image enhancement function. This image enhancement functioncan be a continuous or a discontinuous function. One exemplarydiscontinuous image enhancement function is an image enhancementfunction that makes all grayscale values less than or equal to 250 equalto their original values and all grayscale values greater than 250 equalto 255.

FIG. 1 is a flowchart showing a method 100 for converting a binary pixelof a binary image to a grayscale pixel of a grayscale image andsuppressing background inaccuracies in the grayscale image, inaccordance with an embodiment.

In step 110 of method 100, a binary image is inputted or scanned. Thebinary image is inputted or scanned by a printer, for example. Thebinary image can also be retrieved from a memory, or from a networklocation.

In step 120, a binary pixel of the binary image is associated with aneighbor map. A neighbor map refers to those pixels in proximity to thebinary pixel selected. It can include those pixels bordering theselected pixel, or it may include a broader range. An exemplary neighbormap is shown in FIG. 2.

In step 130, the binary pixel is converted to a grayscale pixel based onthe neighbor map. The binary pixel is a one bit value and the grayscalepixel is an eight bit value, for example.

In step 140, an image enhancement function is applied to the grayscalepixel to suppress background inaccuracies in the grayscale image. Theimage enhancement function is, for example, a tone reproduction curve.Background inaccuracies can include, but are not limited to, spuriousgray grayscale values in a white background that should be perfectlywhite. An image enhancement function can, but is not limited to,suppressing background inaccuracies by making spurious gray grayscalevalues in a white background completely white grayscale values.

In another embodiment, the image enhancement function is incorporateddirectly into an LUT conversion method by adjusting the specific outputvalues of the LUT. This embodiment reduces the amount of imageprocessing required.

Methods for converting the binary pixel to a grayscale pixel based onthe neighbor map have been described in the '159 patent. These methodsinclude an LUT conversion and a filtering conversion. An exemplary LUTconversion includes comparing the neighbor map to one or more binarypatterns of a LUT and assigning a grayscale value corresponding to abinary pattern of the LUT matching the neighbor map to the grayscalepixel. An exemplary filtering conversion includes filtering the neighbormap.

FIG. 2 is an exemplary 3×3 neighbor map 200, in accordance with anembodiment. Map 200 need not be square, as shown, and can have othershapes, such as generally diamond. Pixel 210 of map 200 is the binarypixel being converted. Pixel 210 is shown with a binary value of one.The surrounding eight pixels are the “neighbors” of pixel 210. Thesesurrounding eight pixels are used to determine the grayscale value ofpixel 210. Maps similar to map 200 are used to determine the grayscalevalue of each of the pixels of a binary image. Map 200, therefore,represents a window that is moved from pixel to pixel over the binaryimage to determine the grayscale value of each pixel from theneighboring pixels captured by the window.

The grayscale value is determined from the window represented by map 200in a number of ways including LUT conversion and filtering conversion.In an LUT conversion, for example, map 200 is compared to a 3×3 patternof the LUT to determine the grayscale value of pixel 210. In a filteringconversion, map 200 is passed through a filter to determine thegrayscale value of pixel 210.

In addition to suppressing background inaccuracies it is alsoadvantageous to reduce image noise produced during the binary tograyscale conversion. In another embodiment, noise is reduced byfiltering the image with a filter that smoothes out the noise. Alow-pass filter, for example, removes all abrupt low to high or high tolow transition areas essentially blurring or smoothing sharp linesbetween black and white. Note, however, that the use of a low-passfilter can also smooth out the edges of text that is located in theimage. This low-pass filtering of text can reduce the quality of thetext.

In order to smooth the image while maintaining the sharpness of thetext, in another embodiment the filter is selectively applied in thehighlight areas where the noise is most visible. This method uses afilter in conjunction with a LUT in the one bit to eight bit conversionprocess in order to suppress noise in highlight areas of the image whilemaintaining sharp edges in text regions. The filter is used to smooththe noise and is selectively used in the highlight areas by determiningthe lightness value of the pixel being processed. If the value is abovea threshold, the filter is applied. If the value is below the thresholdvalue, the LUT value is used without filtering. In general, a transitionregion of lightness values can be used. In the transition region thefinal value of the pixel is determined by a weighted combination of theLUT value and the filtered LUT value. This eliminates defects in areasof the image where the lightness is transitioning between a low valueand a high value.

FIG. 3 is schematic diagram showing a system 300 for converting a binarypixel of a binary image to a grayscale pixel of a grayscale image andsuppressing noise in the grayscale image using selective filtering ofthe binary image, in accordance with an embodiment. System 300 includescontrol LUT 310, filter 320, and conversion LUT 330. Filter 320 is usedto determine the grayscale values in lighter areas, conversion LUT 330is used to determine the grayscale values in darker areas, and controlLUT 310 is used to determine the lightness or darkness in an area of thebinary and assign the contributions of filter 320 and conversion LUT 330in determining the grayscale value.

A binary image is scanned or input to system 300 by a printer, forexample. The binary image can also be retrieved from a memory, or from anetwork location. Neighbor map 340 is associated with or created foreach pixel of the binary image. Neighbor map 340 is converted to aweighting value by control LUT 310, to a first grayscale pixel value byfilter 320, and to a second grayscale pixel value by conversion LUT 330.The weighting value, the first grayscale pixel value, and the secondgrayscale pixel value are created at substantially the same time.

The inverted weighting value is created by inverter 350. The invertedweighting value is multiplied by the first grayscale pixel value usingmultiplier 360 producing a first weighted grayscale pixel value. Theuninverted weighting value is multiplied by the second grayscale pixelvalue using multiplier 370 producing a second weighted grayscale pixelvalue. Finally, the first weighted grayscale pixel value and the secondweighted grayscale pixel value are summed by summation 380 to produce agrayscale pixel value.

In system 300, the binary input is sent through conversion LUT 330 andthrough filter 320 to convert the one bit value to an eight bit value.Filter 320 output and conversion LUT 330 output are weighted accordingto the weights determined by the control LUT 310. If the eight bitlightness value is high, filter 320 output gets a high weighting valueand conversion LUT 330 output gets a low weighting value. If the eightbit lightness value is low, filter 320 output gets a low weightingvalue, while conversion LUT 330 output gets a high weighting value. Thismakes the gray output like filter 320 output when the input lightness ishigh and like conversion LUT 330 output when the input lightness is low.

Control LUT 310 can provide an abrupt change in the weighting value or agradual change in the weighting value. A binary control LUT 310 providesa weighting value of one for darker regions up to a certain threshold,for example. Beyond that threshold the binary control LUT 310 provides aweighting value of zero, providing an abrupt change in the weightingvalue and an abrupt change from LUT conversion to filtering. A varyingcontrol LUT 310, on the other hand, provides weighting values betweenzero and one providing a gradual change in weighting values. The use ofa varying control LUT 310 helps to remove defects when the lightnessswitches abruptly from high to low or low to high by allowingcontributions from both LUT conversion and filtering.

FIG. 4 is a flowchart showing a method 400 for converting a binary pixelof a binary image to a grayscale pixel of a grayscale image andsuppressing noise in the grayscale image using selective filtering ofthe binary image, in accordance with an embodiment.

In step 410 or method 400, a binary image is inputted or scanned. Thebinary image is inputted or scanned by a printer, for example. Thebinary image can also be retrieved from a memory, or from a networklocation.

In step 420, a binary pixel of the binary image is associated with aneighbor map. The binary pixel is a one bit value.

In step 430, the neighbor map is converted to a weighting value. Theweighting value is, for example, a value greater than or equal to zeroand less than or equal to one. In another embodiment, the weightingvalue can be one or zero.

The neighbor map is converted to a weighting value using a control LUT,for example. The neighbor map is compared to one or more binary patternsof the control LUT. A weighting value is produced corresponding to abinary pattern of the control LUT that matches the neighbor map.

In another embodiment, the neighbor map is converted to a weightingvalue using a threshold value. The values of the neighbor map are summedto produce a neighbor map value. The neighbor map value is compared tothe threshold value. One is assigned to the weighting value, if theneighbor map value is greater than the threshold value. Zero is assignedto the weighting value, if the neighbor map value is less than or equalto the threshold value.

In another embodiment, the neighbor map is converted to a weightingvalue using a threshold value. The values of the neighbor map are summedto produce a neighbor map value. The neighbor map value is compared tothe threshold value. Zero is assigned to the weighting value, if theneighbor map value is greater than the threshold value. One is assignedto the weighting value, if the neighbor map value is less than or equalto the threshold value.

In step 440, the neighbor map is filtered to produce a first grayscalepixel value. The first grayscale pixel value is an eight bit value, forexample.

In step 450, the neighbor map is compared to one or more binary patternsof a conversion look up table to produce a second grayscale pixel value.

In step 460, an inverted weighting value is multiplied by the firstgrayscale pixel value to produce a first weighted grayscale pixel value.The inverted weighting value is, for example, the weighting valuesubtracted from one.

In step 470, the weighting value is multiplied by the second grayscalepixel value to produce a second weighted grayscale pixel value.

In step 480, the first weighted grayscale pixel value and the secondweighted grayscale pixel value are summed to produce the grayscale pixeland suppress noise.

In another embodiment, filtering is applied to the grayscale image afterit has been assembled using a conversion LUT. Filtering the grayscaleimage provides another level of smoothing at an additional computationcost. Also, rather than using a “continuous” mixing of the filtered andno-filtered data, the decision to select filtered or non-filtered datais improved by looking at the local grayness value. Filtering is appliedonly when a grayscale pixel is fully in a highlight region.

FIG. 5 is schematic diagram showing a system 500 for converting a binaryimage to a first grayscale image and suppressing noise in the firstgrayscale image to produce a noise suppressed grayscale image usingselective filtering of the first grayscale image, in accordance with anembodiment. System 500 includes selector 510, filter 520, and conversionLUT 530. A binary image is scanned or input to system 500 by a printer,for example. The binary image can also be retrieved from a memory, orfrom a network location. A binary neighbor map 540 is associated with orcreated for each pixel of the binary image.

Each binary pixel of the binary image is converted to a first grayscalepixel of a first grayscale image based on each binary neighbor map 540using conversion LUT 530. A grayscale neighbor map (not shown) iscreated for each first grayscale pixel. Each grayscale neighbor map isconverted to a selection value using selector 510. Each grayscaleneighbor map is also filtered using filter 520 to produce a secondgrayscale pixel for each first grayscale pixel. A noise suppressedgrayscale image is produced by selecting each noise suppressed grayscalepixel from either the first grayscale pixel or the second grayscaleusing multiplexer 550. The first grayscale pixel is selected bymultiplexer 550 if the selection value of selector 510 is a firstselection value. The second grayscale pixel is selected by multiplexer550 if the selection value of selector 510 is a second selection value.

FIG. 6 is schematic diagram showing a method 600 for converting a binaryimage to a first grayscale image and suppressing noise in the firstgrayscale image to produce a noise suppressed grayscale image usingselective filtering of the first grayscale image, in accordance with anembodiment.

In step 610 or method 600, a binary image is inputted or scanned. Thebinary image is inputted or scanned by a printer, for example. Thebinary image can also be retrieved from a memory, or from a networklocation.

In step 620, a binary pixel of the binary image is associated with aneighbor map. The binary pixel is a one bit value.

In step 630, each binary pixel of the binary image is converted to afirst grayscale pixel of a first grayscale image based on the binaryneighbor map. A first grayscale pixel is an eight bit value, forexample. The binary image is converted to a first grayscale pixel of afirst grayscale image based on the binary neighbor map using an LUT, forexample. The binary neighbor map is compared to one or more binarypatterns of the LUT. A grayscale value is assigned corresponding to abinary pattern of the LUT matching the binary neighbor map to the eachfirst grayscale pixel.

In step 640, a grayscale neighbor map is created for each firstgrayscale pixel.

In step 650, each grayscale neighbor map is converted to a selectionvalue.

Each grayscale neighbor map is converted to a selection value using athreshold value, for example. Each first grayscale pixel of eachneighbor in the each grayscale neighbor map is compared to a thresholdvalue. The second selection value is assigned to the selection value, ifthe first grayscale pixel of all neighbors in the grayscale neighbor mapare greater than the threshold value.

In another embodiment, each first grayscale pixel of each neighbor inthe each grayscale neighbor map is compared to a threshold value. Thefirst selection value is assigned to the selection value, if the firstgrayscale pixel of all neighbors in the grayscale neighbor map are lessthan the threshold value.

In step 660, each grayscale neighbor map is filtered to produce a secondgrayscale pixel. A second grayscale pixel is an eight bit value, forexample.

In step 670, a noise suppressed grayscale image is produced by selectingeach noise suppressed grayscale pixel as the first grayscale pixel ifthe selection value is a first selection value and selecting each noisesuppressed grayscale pixel as the second grayscale pixel if the eachselection value is a second selection value.

It will be appreciated that various of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. Variouspresently unforeseen or unanticipated alternatives, modifications,variations, or improvements therein may be subsequently made by thoseskilled in the art which are also intended to be encompassed by thefollowing claims.

The claims can encompass embodiments in hardware, software, or acombination thereof.

The word “printer” as used herein encompasses any apparatus, such as adigital copier, bookmaking machine, facsimile machine, multi-functionmachine, etc. Which performs a print outputting function for anypurpose.

Although a monochrome printing apparatus has been described in theSpecification, the claims can encompass embodiments that print in coloror handle color image data. Thus, the terms gray or grayscale refer tocolor tone, and not necessarily to gray as between black and white.

1. A method for converting a binary pixel of a binary image to agrayscale pixel of a grayscale image and suppressing backgroundinaccuracies in the grayscale image, comprising: inputting the binaryimage; associating the binary pixel with a neighbor map; converting thebinary pixel to the grayscale pixel based on the neighbor map; andapplying an image enhancement function to the grayscale pixel tosuppress background inaccuracies in the grayscale image.
 2. The methodof claim 1, wherein the binary pixel comprises a one bit value and thegrayscale pixel comprises an eight bit value.
 3. The method of claim 1,wherein converting the binary pixel to the grayscale pixel based on theneighbor map comprises: comparing the neighbor map to one or more binarypatterns of a look up table; and assigning a grayscale valuecorresponding to a binary pattern of the look up table matching theneighbor map to the grayscale pixel.
 4. The method of claim 1, whereinconverting the binary pixel to the grayscale pixel based on the neighbormap comprises filtering the neighbor map.
 5. The method of claim 1,wherein the image enhancement function comprises a tone reproductioncurve.
 6. The method of claim 1, wherein the image enhancement functioncomprises a continuous function.
 7. The method of claim 1, wherein theimage enhancement function comprises a discontinuous function.
 8. Amethod for converting a binary pixel of a binary image to a grayscalepixel of a grayscale image and suppressing noise in the grayscale image,comprising: inputting the binary image; associating the binary pixelwith a neighbor map; converting the neighbor map to a weighting value;filtering the neighbor map to produce a first grayscale pixel value;comparing the neighbor map to one or more binary patterns of aconversion look up table to produce a second grayscale pixel value;multiplying an inverted weighting value by the first grayscale pixelvalue to produce a first weighted grayscale pixel value; multiplying theweighting value by the second grayscale pixel value to produce a secondweighted grayscale pixel value; and summing the first weighted grayscalepixel value and the second weighted grayscale pixel value to produce thegrayscale pixel and suppress noise.
 9. The method of claim 8, whereinthe binary pixel comprises a one bit value, the first grayscale pixelvalue comprises an eight bit value, and the second grayscale pixel valuecomprises an eight bit value.
 10. The method of claim 8, wherein theweighting value comprises a value greater than or equal to zero and lessthan or equal to one.
 11. The method of claim 10, wherein the invertedweighting value comprises the difference between the weighting value andone.
 12. The method of claim 8, wherein the weighting value comprisesone of one and zero.
 13. The method of claim 8, wherein converting theneighbor map to a weighting value comprises: comparing the neighbor mapto one or more binary patterns of a control look up table; and producinga weighting value corresponding to a binary pattern of the control lookup table matching the neighbor map.
 14. The method of claim 8, whereinconverting the neighbor map to a weighting value comprises: summing thevalues of the neighbor map to produce a neighbor map value; comparingthe neighbor map value to a threshold value; assigning one to theweighting value, if the neighbor map value is greater than the thresholdvalue; and assigning zero to the weighting value, if the neighbor mapvalue is less than or equal to the threshold value.
 15. The method ofclaim 8, wherein converting the neighbor map to a weighting valuecomprises: summing the values of the neighbor map to produce a neighbormap value; comparing the neighbor map value to a threshold value;assigning zero to the weighting value, if the neighbor map value isgreater than the threshold value; and assigning one to the weightingvalue, if the neighbor map value is less than or equal to the thresholdvalue.
 16. A method for converting a binary image to a first grayscaleimage and suppressing noise in the first grayscale image to produce anoise suppressed grayscale image, comprising: inputting the binaryimage; associating each binary pixel of the binary image with a binaryneighbor map; converting the each binary pixel to an each firstgrayscale pixel of the first grayscale image based on the binaryneighbor map; creating an each grayscale neighbor map for the each firstgrayscale pixel; converting the each grayscale neighbor map to an eachselection value; filtering the each grayscale neighbor map to produce aneach second grayscale pixel; and producing the noise suppressedgrayscale image by selecting an each noise suppressed grayscale pixel asthe each first grayscale pixel if the each selection value is a firstselection value and selecting an each noise suppressed grayscale pixelas the each second grayscale pixel if the each selection value is asecond selection value.
 17. The method of claim 16, wherein the eachbinary pixel comprises a one bit value, the each first grayscale pixelcomprises and eight bit value, and the each second grayscale pixelcomprises an eight bit value.
 18. The method of claim 16, whereinconverting an each binary pixel to an each first grayscale pixel basedon the binary neighbor map comprises: comparing the binary neighbor mapto one or more binary patterns of a look up table; and assigning agrayscale value corresponding to a binary pattern of the look up tablematching the binary neighbor map to the each first grayscale pixel. 19.The method of claim 16, wherein converting the each grayscale neighbormap to an each selection value comprises: comparing the each firstgrayscale pixel of each neighbor in the each grayscale neighbor map to athreshold value; and assigning the second selection value to the eachselection value if the each first grayscale pixel of all neighbors inthe each grayscale neighbor map are greater than the threshold value.20. The method of claim 16, wherein converting the each grayscaleneighbor map to an each selection value comprises: comparing the eachfirst grayscale pixel of each neighbor in the each grayscale neighbormap to a threshold value; and assigning the first selection value to theeach selection value if the each first grayscale pixel of all neighborsin the each grayscale neighbor map are less than the threshold value.